Purpose The variability of weather at tourist destinations can significantly affect travel decisions by tourists and their comfort. In particular, rain affects the profitability of hospitality firms that can hardly contrast the phenomenon of heavy rain. Therefore, the assessment of rainfall financial risks, i.e. the negative economic effects caused by rain, becomes crucial to safeguarding the profitability of the hospitality industry. The purpose of this study is to assess such risks. Design/methodology/approach The present work contributes to the literature on weather/climate change and tourism by advancing a model for the rainfall financial risk assessment of hospitality firms. The model is based on scenario correlation between business performances and rain and originates from the Enterprise Risk Management (ERM) presented by the Committee of Sponsoring Organizations of the Treadway Commission (COSO), where some tools to adequately face business risks are advanced. Findings The model is complemented by an empirical experiment based on the business performances of the hospitality industry of Lake Garda and the amount of rainfall in the same area during the decade 2005-2014. The empirical application detects scenario correlation between those variables over time. In particular, the findings open opportunities to purchase financial instruments (insurance contracts, derivative instruments, etc.) with greater awareness, with the purpose of mitigating the negative impacts of rain on business performances of hospitality firms. Originality/value The model improves scenario analysis by introducing scenario correlation, which is a tool for assessing the highly nonlinear links between business performances and rain in today’s complex world. This is the essential step that firms should perform if they want to successfully adopt strategic decisions about rainfall financial risk management.

Globalisation, climate change, the global economic crisis, and the increasing political instability have multiplied the risk factors in the tourism industry. Climate change, in particular the variability of weather, hevily affects decision-making in the tourism industry. Indeed, tourism can be considered a highly weather-sensitive economic sector, but, at the same time, the tourism industry has a key role to play in dealing with the challenges of climate change. For their own survival, tourism firms should adopt actions aimed at promoting a risk reduction through urgent measures designed to combat climate change (reduction of energy consumption, switching to renewable energy sources, etc.). On the other hand, they should enable complementary interventions to share weather risks: from "accessory services", such as tasting events, sport activities, wellness centers, which promote tourism attractiveness safeguarding territory sustainability, to rainfall derivatives, which are designed to protect tourism firms from excessive rainfall.

Markov chain theory is proving to be a powerful approach to bootstrap highly nonlinear time series. In this work we provide a method to estimate the memory of a Markov chain (i.e. its order) and to identify its relevant states. In particular the choice of memory lags and the aggregation of irrelevant states are obtained by looking for regularities in the transition probabilities. Our approach is based on an optimization model. More specifically we consider two competing objectives that a researcher will in general pursue when dealing with bootstrapping:preserving the "structural" similarity between the original and the simulated series and assuring a controlled diversification of the latter. A discussion based on information theory is developed to define the desirable properties for such optimal criteria. Two numerical tests are developed to verify the effectiveness of the method proposed here.MSC classification: 60J10, 60J20, 60J22, 62B10, 62F40, 91G60.

Markov chain theory is proving to be a powerful approach to bootstrap nite states processes, especially where time dependence is non linear. In this work we extend such approach to bootstrap discrete time continuous-valued processes. To this purpose we solve a minimization problem to partition the state space of a continuous-valued process into a nite number of intervals or unions of intervals (i.e. its states) and identify the time lags which provide \memory" to the process. A distance is used as objective function to stimulate the clustering of the states having similar transition probabilities. The problem of the exploding number of alternative partitions in the solution space (which grows with the number of states and the order of the Markov chain) is addressed through a Tabu Search algorithm. The method is applied to bootstrap the series of the German and Spanish electricity prices. The analysis of the results con rms the good consistency properties of the method we propose.

Technical analysis includes a huge variety of trading rules. This fact has always been a serious hindrance to the large number of market efficiency studies implemented either to demonstrate the profitability of market-beating systems or to deny their operational feasibility. For evident reasons it is practically impossible and theoretically weak to systematically analyse the entire body of all trading rules. We therefore propose a novel method to form natural classes of trading rules which are found to be robust to changing market scenarios. In particular, groups are formed adopting a similarity measure based on the investing signals of the trading rules. Our clustering methodology adopts a Markov chain bootstrapping technique to generate differentiated scenarios preserving volume and price joint distributional features. An application is developed on a sample of 674 trading rules. Results show that six groups (here identified as trading styles) are sufficient to explain the large portion of the investing signals variance. We also suggest applications of our results to fund performance measurement and the analysis of financial markets.